抗运动干扰的人脸视频心率估计

杨昭; 杨学志; 霍亮; 刘雪南; 李江山

doi:10.11999/JEIT170824

抗运动干扰的人脸视频心率估计

doi: 10.11999/JEIT170824

基金项目:

合肥工业大学应用科技成果培育计划资助项目(JZ2016YYPY0051)

计量
- 文章访问数: 2058
- HTML全文浏览量: 407
- PDF下载量: 183
- 被引次数: 0
出版历程
- 收稿日期: 2017-08-23
- 修回日期: 2018-02-01
- 刊出日期: 2018-06-19

Heart Rate Estimation from Face Videos Against Motion Interference

Funds:

Training Programme Foundation for Application of Scientific and Technological Achievements of Hefei University of Technology (JZ2016YYPY0051)

摘要

摘要: 该文针对现有的人脸视频心率检测方法在现实情景中受运动干扰难以准确估计心率的问题，提出一种抑制运动干扰的非接触式心率估计新方法。首先利用判别响应图拟合与KLT跟踪算法消除人脸的刚性运动干扰；然后使用对运动鲁棒的色度特征进行两步心率估计，并引入空间梯度因子调控空域和频域的权重，抑制非刚性运动的干扰；最终得到人脸不同区域融合的平均心率数值和信号波形图，实现心率的精确估计。实验结果表明：所提方法相比其它的基于人脸视频的心率估计方法优势明显，提升了信号波形图和真实脉搏波形的一致性，进一步提高了心率估计的精度和鲁棒性。
- 人脸视频检测 /
- 两步心率估计 /
- 光电容积描记 /
- 判别响应图拟合 /
- 色度特征 /
- 空间梯度
Abstract: A novel non-contact heart rate estimation method is proposed to deal with the issue of heart rate measurement from face videos under motion interference in realistic situations, it is hard to estimate heart rate accurately using existing methods. Firstly, the discriminative response map fitting method and KLT tracking algorithm are used to eliminate the influence of face rigid motion. Then the chrominance features are selected to estimate heart rate with two steps because of the robustness to facial movements. The frequency and spatial domain weights are assigned through spatial gradient to eliminate the influence of non-rigid motion. Finally, the accurate average heart rate value and pulse wave signal waveform can be acquired from different face regions. Compared with three other methods, experimental results indicate that the proposed method enhances the consistency of estimated waveform and ground truth waveform and has obvious superiority in accuracy and robustness of heart rate estimation.
- Face video detection /
- Two-step heart rate estimation /
- PhotoPlethysmoGraphy (PPG) /
- Discriminative Response Map Fitting (DRMF) /
- Chrominance features /
- Spatial gradient

HTML全文

参考文献(18)

KANNEL W B, KANNEL C, PAFFENBERGER R S, et al. Heart rate and cardiovascular mortality: The framingham study[J]. American Heart Journal, 1987, 113(6): 1489-1494. doi: 10.1016/0002-8703(87)90666-1.

TEMKO A. Accurate heart rate monitoring during physical exercises using PPG[J]. IEEE Transactions on Biomedical Engineering, 2017, 64(9): 2016-2024. doi: 10.1109/TBME. 2017.2676243.

ALLEN J. Photoplethysmography and its application in clinical physiological measurement[J]. Physiological Measurement, 2007, 28(3): 1-39. doi: 10.1088/0967-3334/28/ 3/R01.

VERKRUYSSE W, SVAASAND L O, and NELSON J S. Remote plethysmographic imaging using ambient light[J]. Opt Express, 2008, 16(26): 21434-21445. doi: 10.1364/OE.16. 021434.

POH M Z, MCDUFF D J, and PICARD R W. Non-contact, automated cardiac pulse measurements using video imaging and blind source separation[J]. Optics Express, 2010, 18(10): 10762-10774. doi: 10.1364/OE.18.010762.

POH M Z, MCDUFF D J, and PICARD R W. Advancements in noncontact, multiparameter physiological measurements using a webcam[J]. IEEE Transactions on Biomedical Engineering, 2011, 58(1): 7-11. doi: 10.1109/ TBME.2010.2086456.

LI X, CHEN J, ZHAO G, et al. Remote heart rate measurement from face videos under realistic situations[C]. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 4264-4271. doi: 10.1109/CVPR.2014.543.

CHWYL B, CHUNG A G, AMELARD R, et al. SAPPHIRE: Stochastically acquired photoplethysmogram for heart rate inference in realistic environments[C]. IEEE International Conference on Image Processing, Phoenix, USA, 2016: 1230-1234. doi: 10.1109/ICIP.2016.7532554.

TULYAKOV S, ALAMEDA-PINEDA X, RICCI E, et al. Self-adaptive matrix completion for heart rate estimation from face videos under realistic conditions[C]. IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 2396-2404. doi: 10.1109/CVPR.2016. 263.

CHENG J, CHEN X, XU L, et al. Illumination variation- resistant video-based heart rate measurement using joint blind source separation and ensemble empirical mode decomposition[J]. IEEE Journal of Biomedical and Health Informatics, 2016, 21(5): 1422-1433. doi: 10.1109/JBHI.2016. 2615472.

JEONG I C and FINKELSTEIN J. Introducing contactless blood pressure assessment using a high speed video camera[J]. Patient Facing Systems, 2016, 40(4): 77:1-77:10. doi: 10.1007/s10916-016-0439-z.

ZHANG C, WU X, ZHANG L, et al. Simultaneous detection of blink and heart rate using multi-channel ICA from smart phone videos[J]. Biomedical Signal Processing and Control, 2017, 33(3): 189-200. doi: 10.1016/j.bspc.2016.11.022.

VIOLA P and JONES M. Rapid object detection using a boosted cascade of simple features[C]. IEEE Conference on Computer Vision and Pattern Recognition, Kauai, USA, 2001: 511-518. doi: 10.1109/CVPR.2001.990517.

ASTHANA A, ZAFEIRIOU S, CHENG S, et al. Robust discriminative response map fitting with constrained local models[C]. IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 3444-3451. doi: 10.1109/CVPR.2013.442.

TOMASI C and KANADE T. Detection and tracking of point features[J]. Technical Report, 1991, 91(21): 9795-9802.

FISCHLER M A and BOLLES R C. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 1981, 24(6): 381-395. doi: 10.1145/358669.358692.

HAAN G D and JEANNE V. Robust pulse rate from chrominance-based rPPG[J]. IEEE Transactions on Biomedical Engineering, 2013, 60(10): 2878-2886. doi: 10.1109/TBME.2013.2266196.

BRENNAN D. Linear diversity combining techniques[J]. Proceedings of the IEEE, 2003, 91(2): 331-356. doi: 10.1109/ JRPROC.2002.808163.

施引文献

资源附件(0)

访问统计